Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Is there any advantage to combined trastuzumab and chemotherapy in perioperative setting her 2neu positive localized gastric adenocarcinoma?

We report here a 44-year-old Moroccan man with resectable gastric adenocarcinoma with overexpression of human epidermal growth factor receptor 2 (HER2) by immunohistochemistry who was treated with trastuzumab in combination with chemotherapy in perioperative setting. He received 3 cycles of neoadjuvant chemotherapy consisting of trastuzumab, oxaliplatin, and capecitabine. Afterwards, he received total gastrectomy with extended D2 lymphadenectomy without spleno-pancreatectomy. A pathologic complete response was obtained with a combination of trastuzumab and oxaliplatin and capecitabine. He received 3 more cycles of trastuzumab containing regimen postoperatively

Statistical machines for trauma hospital outcomes research: Application to the PRospective, Observational, Multi-center Major trauma Transfusion (PROMMTT) study

Improving the treatment of trauma, a leading cause of death worldwide, is of great clinical and public health interest. This analysis introduces flexible statistical methods for estimating center-level effects on individual outcomes in the context of highly variable patient populations, such as those of the PRospective, Observational, Multi-center Major Trauma Transfusion study. Ten US level I trauma centers enrolled a total of 1,245 trauma patients who survived at least 30 minutes after admission and received at least one unit of red blood cells. Outcomes included death, multiple organ failure, substantial bleeding, and transfusion of blood products. The centers involved were classified as either large or small-volume based on the number of massive transfusion patients enrolled during the study period. We focused on estimation of parameters inspired by causal inference, specifically estimated impacts on patient outcomes related to the volume of the trauma hospital that treated them. We defined this association as the change in mean outcomes of interest that would be observed if, contrary to fact, subjects from large-volume sites were treated at small-volume sites (the effect of treatment among the treated). We estimated this parameter using three different methods, some of which use data-adaptive machine learning tools to derive the outcome models, minimizing residual confounding by reducing model misspecification. Differences between unadjusted and adjusted estimators sometimes differed dramatically, demonstrating the need to account for differences in patient characteristics in clinic comparisons. In addition, the estimators based on robust adjustment methods showed potential impacts of hospital volume. For instance, we estimated a survival benefit for patients who were treated at large-volume sites, which was not apparent in simpler, unadjusted comparisons. By removing arbitrary modeling decisions from the estimation process and concentrating on parameters that have more direct policy implications, these potentially automated approaches allow methodological standardization across similar comparativeness effectiveness studies

BCOR regulates myeloid cell proliferation and differentiation

BCOR is a component of a variant Polycomb group repressive complex 1 (PRC1). Recently, we and others reported recurrent somatic BCOR loss-of-function mutations in myelodysplastic syndrome and acute myelogenous leukaemia (AML). However, the role of BCOR in normal hematopoiesis is largely unknown. Here, we explored the function of BCOR in myeloid cells using myeloid murine models with Bcor conditional loss-of-function or overexpression alleles. Bcor mutant bone marrow cells showed significantly higher proliferation and differentiation rates with upregulated expression of Hox genes. Mutation of Bcor reduced protein levels of RING1B, an H2A ubiquitin ligase subunit of PRC1 family complexes and reduced H2AK119ub upstream of upregulated HoxA genes. Global RNA expression profiling in murine cells and AML patient samples with BCOR loss-of-function mutation suggested that loss of BCOR expression is associated with enhanced cell proliferation and myeloid differentiation. Our results strongly suggest that BCOR plays an indispensable role in hematopoiesis by inhibiting myeloid cell proliferation and differentiation and offer a mechanistic explanation for how BCOR regulates gene expression such as Hox genes